In the real world, relationships matter: blog posts have comments, bank
accounts have transactions, customers have bank accounts, orders have order
lines, and directories have files and subdirectories.

Relational databases are specifically designed—and this will not come as a
surprise to you—to manage relationships:

Each entity (or row, in the relational world) can be uniquely identified
by a primary key.

Entities are normalized. The data for a unique entity is stored only
once, and related entities store just its primary key. Changing the data of
an entity has to happen in only one place.

Entities can be joined at query time, allowing for cross-entity search.

Changes to a single entity are atomic, consistent, isolated, and
durable. (See ACID Transactions
for more on this subject.)

Most relational databases support ACID transactions across multiple
entities.

But relational databases do have their limitations, besides their poor support
for full-text search. Joining entities at query time is expensive—the more
joins that are required, the more expensive the query. Performing joins
between entities that live on different hardware is so expensive that it is
just not practical. This places a limit on the amount of data that can be
stored on a single server.

Elasticsearch, like most NoSQL databases, treats the world as though it were
flat. An index is a flat collection of independent documents. A single
document should contain all of the information that is required to decide
whether it matches a search request.

While changing the data of a single document in Elasticsearch is
ACIDic, transactions
involving multiple documents are not. There is no way to roll back the index
to its previous state if part of a transaction fails.

This FlatWorld has its advantages:

Indexing is fast and lock-free.

Searching is fast and lock-free.

Massive amounts of data can be spread across multiple nodes, because each
document is independent of the others.

But relationships matter. Somehow, we need to bridge the gap between
FlatWorld and the real world. Four common techniques are used to manage
relational data in Elasticsearch: